The Electronic Journal of e-Learning provides perspectives on topics relevant to the study, implementation and management of e-Learning initiatives
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Journal Article

A Learning Analytics Approach to the Evaluation of an Online Learning Package in a Hong Kong University  pp11-24

Dennis Foung, Julia Chen

© Jan 2019 Volume 17 Issue 1, Editor: Rikke Ørngreen and Karin Levinsen, pp1 - 63

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Abstract

In recent years, research using learning analytics to predict learning outcomes has begun to increase. This emerging field of research advocates the use of readily‑available data to inform teaching and learning. The current case study adopts a learning analytics approach to evaluate the online learning package of an academic English course in a university in Hong Kong. This study aims to (1) explore the completion pattern of use of the online learning package by students in a generic undergraduate academic skills course; and (2) predict student outcomes based on their online behaviour patterns. Over three academic years, the study examined usage logs for 7000+ students that were available on the university’s learning management system. Student assessment component scores, online activity completion rates, and online behavioural patterns were identified and examined using descriptive analysis, bivariate correlation analysis, and multiple regression analysis. The findings reveal insights into different online learning behavioural patterns that would benefit blended course designers. For instance, some students started using the online learning package early in the semester but fulfilled only the minimum required online work, whereas others greatly exceeded the basic requirement and continued doing activities in the online package even after the semester had finished. The relationship between learning activities in the online package and assessment component grades was found to be weak but meaningful. A regression model was developed drawing on the completion rates to predict overall student scores, and this model successfully identified several specific factors, such as total number of attempts and performance in individual online learning activities, as predictors of the final course grade.

 

Keywords: learning analytics, blended learning; online learning package, English for academic purposes, Hong Kong, course design

 

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Journal Article

Identifying Consistent Variables in a Heterogeneous Data Set: Evaluation of a Web‑Based Pre‑Course in Mathematics  pp82-93

Katja Derr

© Apr 2017 Volume 15 Issue 1, Editor: Robert Ramberg, pp1 - 103

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Abstract

E‑learning has made course evaluation easier in many ways, as a multitude of learner data can be collected and related to student performance. At the same time, open learning environments can be a difficult field for evaluation, with a large variance in participants’ knowledge level, learner behaviour, and commitment. In this study the effectiveness of a mathematics pre‑course administered to four cohorts of prospective students at a technical faculty in Germany was evaluated. Deficits in basic mathematics knowledge are considered one risk factor regarding graduation in STEM‑related subjects, thus the overall goal was to investigate if the pre‑course enabled “at risk” students to improve their starting position. A data analysis was performed, relating students’ preconditions when entering university, their attitude towards mathematics, and their use of learning strategies with further study success. The strongest determinant of first year performance were results in a diagnostic pretest, confirming both the importance of basic mathematics knowledge for academic achievement in engineering and the reliability of the chosen pre‑posttest design. Other outcomes were quite unexpected and demanded deeper analyses. Students who had participated in additional face‑to‑face courses, for example, showed less learning gains than students who had participated in an e‑tutoring version. It also could be observed that meta‑cognitive variables failed to explain successful course participation. Reasons for these outcomes are discussed, suggesting reliability threats and interactions between students’ preconditions and their learner behaviour. A significant and unmoderated impact on students’ learning gains in the pre‑course was found for the number of online test attempts, making this variable a reliable indicator of student engagement. The evaluations show that open learning designs with heterogeneous learner groups can deliver meaningful information, provided that limitations are considered and that external references, like academic grades, are available in order to establish consistency.

 

Keywords: learning analytics, pre-course, mathematics, formative e-assessment, STEM

 

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Journal Issue

Volume 15 Issue 1 / Apr 2017  pp1‑103

Editor: Robert Ramberg

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Keywords: academic staff, attitudes, clinical education, communication, communities, competencies, courses, critical theory, decision-making, Distance learning, e-learning, e-learning projects, e-learning research, E-Learning team, ethical issues, ethnography, expectations, formative e-assessment, Foucault, gaps, health promotion, learning analytics, major project issues, mathematics, Mobile eye tracking methods, motivation, motivation to learn, motivational gap, new model, online distance learning, pedagogy, perception bias, power, pre-course, qualitative research, quality, quality indicators, quality of e-learning, research methodology, satisfaction, service, socio-cultural contexts, staff development, STEM, technology, theory development, training management, training motivation, visitor studies, visual ethnography

 

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